Last updated: 2024-02-27
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Knit directory: PD1_mm/
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| absolute | relative |
|---|---|
| /home/hnatri/PD1_mm/ | . |
| /home/hnatri/PD1_mm/code/utilities.R | code/utilities.R |
| /home/hnatri/PD1_mm/code/PD1_mm_themes.R | code/PD1_mm_themes.R |
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 18c1bd4 | heinin | 2024-02-27 | Cell type proportion testing |
| html | 18c1bd4 | heinin | 2024-02-27 | Cell type proportion testing |
| Rmd | 3e207b9 | heinin | 2024-02-26 | Added scImmuCC annotations |
| Rmd | 196db6a | heinin | 2024-02-26 | Starting the comparative analysis |
Adding annotations and comparing treatment groups/timepoints.
suppressPackageStartupMessages({
#library(cli)
library(Seurat)
library(SeuratObject)
library(SeuratDisk)
library(tidyverse)
library(tibble)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(workflowr)
library(googlesheets4)
library(scProportionTest)})
setwd("/home/hnatri/PD1_mm/")
set.seed(9999)
options(ggrepel.max.overlaps = Inf)
# Colors, themes, cell type markers, and plot functions
source("/home/hnatri/PD1_mm/code/utilities.R")
source("/home/hnatri/PD1_mm/code/PD1_mm_themes.R")
source("/home/hnatri/PD1_mm/code/CART_plot_functions.R")
seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")
celltypes <- sort(as.character(unique(seurat_data$celltype)))
plot_colors <- colorRampPalette(brewer.pal(11, "Paired"))(length(celltypes))
names(plot_colors) <- celltypes
DimPlot(seurat_data,
group.by = "celltype",
reduction = "umap",
raster = T,
label = T,
cols = plot_colors) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend()
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
DimPlot(seurat_data,
group.by = "celltype",
split.by = "Treatment",
reduction = "umap",
raster = T,
#label = T,
cols = plot_colors) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend()
In scatter plots, the proportions of cell types in each pair of treatment groups are plotted against each other with one group on each axis. The forest plots show the significance level.
unique(seurat_data$Treatment)
[1] "ADJ" "CTRL" "NEO"
unique(seurat_data$Day)
[1] 16 12
table(seurat_data$celltype, seurat_data$Treatment)
ADJ CTRL NEO
M1 1090 2163 1603
Mono1 436 1444 2735
M2 910 1634 1452
L1 571 1420 1475
Neut1 526 1498 1321
L2 1117 1045 1063
NK 579 992 1167
M3 449 1117 1054
M4 502 921 1107
M5 252 665 1454
NOS1 251 1619 498
DC 379 773 772
M6 132 763 590
B1 311 568 553
Treg 178 521 523
M7 160 403 615
B2 171 320 347
L3 269 305 225
L4 50 154 371
M8 20 281 185
create_clusterpropplot(seurat_data,
group_var = "Treatment",
group1 = "ADJ",
group2 = "CTRL",
plot_var = "celltype",
plot_colors = plot_colors,
var_names = c("ADJ", "CTRL"),
legend_title = "Treatment")
Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
create_clusterpropplot(seurat_data,
group_var = "Treatment",
group1 = "NEO",
group2 = "CTRL",
plot_var = "celltype",
plot_colors = plot_colors,
var_names = c("NEO", "CTRL"),
legend_title = "Treatment")
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
create_clusterpropplot(seurat_data,
group_var = "Treatment",
group1 = "ADJ",
group2 = "NEO",
plot_var = "celltype",
plot_colors = plot_colors,
var_names = c("ADJ", "NEO"),
legend_title = "Treatment")
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
# Day 12
create_clusterpropplot(subset(seurat_data, subset = Day == 12),
group_var = "Treatment",
group1 = "NEO",
group2 = "CTRL",
plot_var = "celltype",
plot_colors = plot_colors,
var_names = c("NEO", "CTRL"),
legend_title = "Treatment, Day 12")
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
# Day 16
create_clusterpropplot(subset(seurat_data, subset = Day == 16),
group_var = "Treatment",
group1 = "ADJ",
group2 = "CTRL",
plot_var = "celltype",
plot_colors = plot_colors,
var_names = c("ADJ", "CTRL"),
legend_title = "Treatment, Day 16")
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
create_clusterpropplot(subset(seurat_data, subset = Day == 16),
group_var = "Treatment",
group1 = "NEO",
group2 = "CTRL",
plot_var = "celltype",
plot_colors = plot_colors,
var_names = c("NEO", "CTRL"),
legend_title = "Treatment, Day 16")
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
create_clusterpropplot(subset(seurat_data, subset = Day == 16),
group_var = "Treatment",
group1 = "ADJ",
group2 = "NEO",
plot_var = "celltype",
plot_colors = plot_colors,
var_names = c("ADJ", "NEO"),
legend_title = "Treatment, Day 16")
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
# Using scProportionTest
prop_test <- sc_utils(seurat_data)
prop_test <- permutation_test(
prop_test, cluster_identity = "celltype",
sample_1 = "CTRL", sample_2 = "ADJ",
sample_identity = "Treatment")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
#NoLegend() +
ggtitle("ADJ vs. CTRL, all timepoints")
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
prop_test <- permutation_test(
prop_test, cluster_identity = "celltype",
sample_1 = "CTRL", sample_2 = "NEO",
sample_identity = "Treatment")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
#NoLegend() +
ggtitle("NEO vs. CTRL, all timepoints")
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
prop_test <- permutation_test(
prop_test, cluster_identity = "celltype",
sample_1 = "ADJ", sample_2 = "NEO",
sample_identity = "Treatment")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
#NoLegend() +
ggtitle("NEO vs. ADJ, all timepoints")
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
# Day 12 only
# For day 12, no ADJ sample
prop_test <- sc_utils(subset(seurat_data, subset = Day == 12))
prop_test <- permutation_test(
prop_test, cluster_identity = "celltype",
sample_1 = "CTRL", sample_2 = "NEO",
sample_identity = "Treatment")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
#NoLegend() +
ggtitle("NEO vs. CTRL, day 12")
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
# Day 16 only
prop_test <- sc_utils(subset(seurat_data, subset = Day == 16))
prop_test <- permutation_test(
prop_test, cluster_identity = "celltype",
sample_1 = "CTRL", sample_2 = "ADJ",
sample_identity = "Treatment")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
#NoLegend() +
ggtitle("ADJ vs. CTRL, day 16")
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
prop_test <- permutation_test(
prop_test, cluster_identity = "celltype",
sample_1 = "CTRL", sample_2 = "NEO",
sample_identity = "Treatment")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
#NoLegend() +
ggtitle("NEO vs. CTRL, day 16")
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
prop_test <- permutation_test(
prop_test, cluster_identity = "celltype",
sample_1 = "ADJ", sample_2 = "NEO",
sample_identity = "Treatment")
perm_plot <- permutation_plot(prop_test)
perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
#NoLegend() +
ggtitle("NEO vs. ADJ, day 16")
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ComplexHeatmap_2.18.0 viridis_0.6.3
[3] viridisLite_0.4.2 circlize_0.4.15
[5] plyr_1.8.8 RColorBrewer_1.1-3
[7] scProportionTest_0.0.0.9000 googlesheets4_1.1.0
[9] workflowr_1.7.1 ggrepel_0.9.3
[11] ggpubr_0.6.0 lubridate_1.9.2
[13] forcats_1.0.0 stringr_1.5.0
[15] dplyr_1.1.2 purrr_1.0.1
[17] readr_2.1.4 tidyr_1.3.0
[19] tibble_3.2.1 ggplot2_3.4.2
[21] tidyverse_2.0.0 SeuratDisk_0.0.0.9021
[23] Seurat_5.0.1 SeuratObject_5.0.1
[25] sp_1.6-1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.20 splines_4.3.0 later_1.3.1
[4] cellranger_1.1.0 polyclip_1.10-4 fastDummies_1.7.3
[7] lifecycle_1.0.3 rstatix_0.7.2 doParallel_1.0.17
[10] rprojroot_2.0.3 globals_0.16.2 processx_3.8.1
[13] lattice_0.21-8 hdf5r_1.3.8 MASS_7.3-60
[16] backports_1.4.1 magrittr_2.0.3 plotly_4.10.2
[19] sass_0.4.6 rmarkdown_2.22 jquerylib_0.1.4
[22] yaml_2.3.7 httpuv_1.6.11 sctransform_0.4.1
[25] spam_2.9-1 spatstat.sparse_3.0-1 reticulate_1.29
[28] cowplot_1.1.1 pbapply_1.7-0 abind_1.4-5
[31] Rtsne_0.16 BiocGenerics_0.48.1 git2r_0.32.0
[34] S4Vectors_0.40.2 IRanges_2.36.0 irlba_2.3.5.1
[37] listenv_0.9.0 spatstat.utils_3.0-3 goftest_1.2-3
[40] RSpectra_0.16-1 spatstat.random_3.1-5 fitdistrplus_1.1-11
[43] parallelly_1.36.0 leiden_0.4.3 codetools_0.2-19
[46] tidyselect_1.2.0 shape_1.4.6 farver_2.1.1
[49] stats4_4.3.0 matrixStats_1.0.0 spatstat.explore_3.2-1
[52] googledrive_2.1.0 jsonlite_1.8.5 GetoptLong_1.0.5
[55] ellipsis_0.3.2 progressr_0.13.0 iterators_1.0.14
[58] ggridges_0.5.4 survival_3.5-5 foreach_1.5.2
[61] tools_4.3.0 ica_1.0-3 Rcpp_1.0.10
[64] glue_1.6.2 gridExtra_2.3 xfun_0.39
[67] withr_2.5.0 fastmap_1.1.1 fansi_1.0.4
[70] callr_3.7.3 digest_0.6.31 timechange_0.2.0
[73] R6_2.5.1 mime_0.12 colorspace_2.1-0
[76] scattermore_1.2 tensor_1.5 spatstat.data_3.0-1
[79] utf8_1.2.3 generics_0.1.3 data.table_1.14.8
[82] httr_1.4.6 htmlwidgets_1.6.2 whisker_0.4.1
[85] uwot_0.1.14 pkgconfig_2.0.3 gtable_0.3.3
[88] lmtest_0.9-40 htmltools_0.5.5 carData_3.0-5
[91] dotCall64_1.0-2 clue_0.3-64 scales_1.2.1
[94] png_0.1-8 knitr_1.43 rstudioapi_0.14
[97] rjson_0.2.21 tzdb_0.4.0 reshape2_1.4.4
[100] nlme_3.1-162 cachem_1.0.8 zoo_1.8-12
[103] GlobalOptions_0.1.2 KernSmooth_2.23-21 parallel_4.3.0
[106] miniUI_0.1.1.1 pillar_1.9.0 vctrs_0.6.2
[109] RANN_2.6.1 promises_1.2.0.1 car_3.1-2
[112] xtable_1.8-4 cluster_2.1.4 evaluate_0.21
[115] cli_3.6.1 compiler_4.3.0 rlang_1.1.1
[118] crayon_1.5.2 future.apply_1.11.0 ggsignif_0.6.4
[121] labeling_0.4.2 ps_1.7.5 getPass_0.2-4
[124] fs_1.6.2 stringi_1.7.12 deldir_1.0-9
[127] munsell_0.5.0 lazyeval_0.2.2 spatstat.geom_3.2-1
[130] Matrix_1.6-5 RcppHNSW_0.5.0 hms_1.1.3
[133] patchwork_1.1.2 bit64_4.0.5 future_1.32.0
[136] shiny_1.7.4 highr_0.10 ROCR_1.0-11
[139] gargle_1.4.0 igraph_1.4.3 broom_1.0.4
[142] bslib_0.4.2 bit_4.0.5